Abstract

The Park Grass Experiment (PGE), begun at Rothamsted Experimental Station in 1856 and still running, affords a unique opportunity to test for the influence of species number and soil reaction on biomass variability in a suite of comparable plant communities. Biomass variability was measured by calculating the coefficient of variation (CV) over time of annual hay yield in an eleven-year moving window. CV and species number were both strongly negatively correlated with biomass; both relations were affected by time and pH. Multiple regression of CV on species number and mean biomass for nonacidified plots in 42 years between 1862 and 1991 showed a relationship between biomass and CV which was negative in most years and significantly so in nearly three quarters of them (30/42). We are unable to tell how much of this effect is intrinsic to the statistical relation between the mean and CV of biomass. Species number was negatively correlated with CV in 29/42 years, but this was statistically significant on only three occasions. Because this relation was highly significant in the year (1991) for which we have the largest sample size (34 plots), we tentatively conclude that biomass variability may be lower in more species-rich communities, although the effect is possibly a weak one. We suggest that physiological stresses imposed by low pH may explain the greater variability of plots with acidified soil. An increase in the variability of biomass that occurred across plots with time may be due in part to acidification across the whole experiment. Three hypotheses are proposed to explain the relationship between species richness and biomass variability: (i) biomass variability on more species-rich plots is better buffered against climatic variation because species differ in their response to climatic conditions: (ii) there are fewer species on plots with greater biomass variability because species have been lost by competitive exclusion in years when biomass reaches high values; (iii) species richness and variability are both correlated with a third variable, for example soil moisture deficit within a plot. All three hypotheses are susceptible to testing within the PGE.